>>>>>==============>
Note: If you requested a CD-ROM of the Chance Lectures
and have not received it, please send another request to
jlsnell@dartmouth.edu with the address where it should be
sent. Others who would like this CD-ROM are also invited
to send such a request. There is no charge.
<<<========<<

...it was revealed that seven people from the city
(Sheffield) were fatally injured at work last year.

...the figures are for one year only and may not be
typical, but they may reflect a disturbing trend.

Sheffield Star
2 March 1999

Professor Robert Winston is obviously a very clever
man, but I disagree with the calculations of the
probability of the Jim twins' amazing life stories
(The Secret Life of the Twins, 21 July).

If the probability of owning the car they both owned
is 1 in 7, the probability of both twins owning the
same car is 1 in 7, not 1 in 49 as stated by the professor.

Reply from programme's producer:

The 1 in 49 odds quoted were correct for two men, by
chance, driving a Chevrolet. The odds of two men both
driving any make of car depends on the number of makes
of cars and sales of each made available at the time.
The script was checked with a statistician.

The Radio Times (letter)
14-20 August 1999

The number of automatic plant shutdowns(scrams) remained
at a median of zero for the second year running, with 61%
of plants experiencing no scrams.

Nuclear Europe Worldscam
July/August1999

Since it is not always completely clear why a particular item
makes the Forsooth column, Norton Star has volunteered to give his
explanation for any items you find mysterious. His e-mail address
is NSTARR@amherst.edu.
<<<========<<

>>>>>==============>
The "Statistical Education Research Newsletter" is a new
publication of the International Association for Statistical
Education (IASE). This newsletter is an outgrowth of a similar
newsletter started in 1996 by our Chance colleague Joan Garfield.
The newsletter will include summaries of recent research papers,
books, dissertations, bibliographies on specific topics,
information about recent and future conferences, and interesting
internet resources. It will also include short research papers.
You can learn more about this newsletter and find the first issue
at
homepage for The IASE Statistical Education Research Group.
<<<========<<

>>>>>==============>
We would like to mention some other sources of chance news.

Warren Page edits a column "Media Highlights" in "The College
Mathematics Journal" published by the Mathematical Association of
America. In the November 1999 issue (Vol. 30, No.5) there are two
articles of interest to our readers. Here they are.

Surgical Audit: Statistical Lessons from Nightingale and Codman,
David J. Spiegelhalter. Statistics in Society (Journal of the
Royal Statistical Society, Series A) 162:1 (1999) 45-58.

After giving the Florence Nightingale quote we used for this
Chance News, reviewer Tom Short writes:

Ernest Codman (1869-1940) was a Boston surgeon who
championed what Speigelhalter calls the "End Results
Idea" for monitoring surgical outcomes. Codman's
"clinical" approach was to follow every patient's
case history after surgery and to identify individual
surgeon's errors on specific patients. In contrast,
Nightingale advocated an "epidemiological" approach
to surgical audits, focusing on summary statistics
of mortality rates and demographics. As modern health
care systems face the challenge of surgical audits,
Speigelhalter reminds us that the tension between
examining individual cases and summary statistics
dates back more than a century. His article guides
us through the statistical careers of Nightingale
and Codman. There is a fascinating case study that
tracks one surgeon's success rate for a difficult
surgery, and concludes with a recommendation for a
synthesis between the epidemiological and clinical
approaches to reporting surgical outcomes. A
historical perspective may not relieve the
statistical tension in the health care industry, but it
should help to provide context and direction for
the arguers.

It is refreshing to read an article about a census that
does not argue the legal and political ramifications of
urban undercount in the U.S., but it is surprising to
learn just how different the issues of concern for the
census in China.

Tom goes on to indicate what some of these differences are.
<<<========<<

>>>>>==============>
Next we remind our readers to watch John Paulos' monthly
column for on-line ABC news. John's column appears on the first
of each month. His Jan 1, 2000 column is based on the recent
National Academy of Sciences well-publicized report stating that
each year, between 40,000 and 100,000 deaths in this country are a
consequence of medical error. As usual, Paulos has an interesting
and original way to look at this problem. You can find his
discussion at:
Who's Counting?
<<<========<<

>>>>>==============>
Finally, Chance Magazine has a column called "Chance Musings
From the Press." In the latest issue, Fall 1999, we find several
interesting articles discussed. While most are serious, this one
might be considered humorous:

"Play the Odds on the New York Subway"

Al O'Leary who handles public relations for the
New York City Transit Authority, was surprised
at all the phone calls about the dead man who
rode the subway Monday morning. "It's not unusual,"
he said. "We move 4.2 million people every day.
How could you not find a diseased person now and
then?"
The Washington Post, June 16, 1999

<<<========<<

>>>>>==============>
In this same issue of Chance Magazine we enjoyed the following
article:

Howard begins his article by telling us that Fred Mostellar is supposed
to have said that by leaving the Princeton math department to join the
math department at Harvard he succeeded in raising the IQ in both
places. Alas, we thought we had tracked the history of the great
statistical ploy to the former San Francisco Chronicle writer Herb Caen
(see
Chance News 5.03). We must admit that Fred Mostellar is a more
likely person to have thought it up.

Wainer says:

a good display has many purposes but it reaches
its highest value when it forces you to see
something you weren't expecting.

This happened to Wainer when he plotted a graph showing the number
of SAT exams taken from 1960 to 1999. He noted that the number
seemed to be picking up in recent years, after being more or less
constant (about a million a year) for a number of years. However,
he also saw a large bulge (a sixty percent increase followed by a
similar decrease) occurring in the 60's during the period of the
Vietnam War.

To look further, Wainer plotted graphs of the SAT scores and the
percent of male recruits who scored above the median on the Armed
Forces Qualifying Exam (AFQE). In each case the graphs showed a
significant decrease during the Vietnam war period. The decrease
in the AFQE is presumably caused because some of the cream of the
military were siphoned off into the college population by the
Vietnam war. The lowering of the SAT scores then might be
attributed to the Mosteller effect: the Vietnam war lowered the
average intelligence of both the college students and the
military. Wainer notes that this appears to be peculiar to the
Vietnam war since similar data suggests an opposite effect during
World War II.
<<<========<<

>>>>>==============>
Bob Hayden sends us statistical cartoons: His latest is a Dilbert
cartoon with the text:

Dilbert is responding to a paid interactive commercial on the tube.

Studies ave shown that monkeys can pick
stocks better than most professionals.

That's why the Dogbert Mutual Fund employs
only monkeys.

Yes, our fees are high, but I don't apologize
for hiring the best.

Here's a Dilbert cartoon we noticed in this morning's paper
(Jan 3,2000):

Dogbert consults.

You need to do data mining to uncover hidden
treasures.

If you mine the data hard enough you can
also find messages from God.

...sales to left handed squirrels are up...
and God says your tie doesn't go with that
vest.

<<<========<<

>>>>>==============>
In the last Chance News Harold Brooks sent us a note about a
recent NBC Evening News program with Tom Brokaw where the
incidence of breast cancer and death rate from breast cancer on
Long Island were compared to National averages with the
implications that they were significantly higher. Harold wondered
how they determined that the differences are significant. Milton
Eisner is a health statistician at the National Cancer Institute
and sent us an answer to Harold's question. This was provided by
the explanatory notes to the SEER Cancer Statistical Review. The
review which includes these notes is available
here.

Here is how these notes say that significance is tested:

The percent difference (PD) between the individual states
and the rate for the total U.S. is based on the formula

PD = 100(State rate - Total U.S. rate/Total U.S. rate.

The standard error provided for each age-adjusted rate
are calculated, based on the assumption that, for each
age-adjusted rate, the number of deaths is a Poisson
variable with the variances of the age-adjusted rates
being a linear combination of the variances of the age
specific rates. The difference between each age-adjusted
state rate and the age-adjusted total U.S. rate is
tested for statistical significance by calculating a Z
statistic from the following formula:

Z = (State rate - Total U.S. rate/SE_d

The notes point out sources of error that can enter into these
rate values. Errors in the numerator come from miss-classification
of the disease, under-registration of deaths etc. Errors in the
denominator can occur from the under-and over-enumeration in the
census. This would be particularly serious when comparing rates by
race.

The notes also discuss a number of issues in interpreting the
results of the significance tests. They warn that these rate
significance tests are not appropriate for assessing geographic
clustering.

Finally, they note that for many cancers the District of Columbia
is found to have the highest mortality rates. In some cases this
can be explained simply by the fact that mortality rates tend to
be higher in urban areas and DC is predominately urban.

Milt Eisner also pointed out that the death rates in the
L.A. Times article "Trying to map elusive N.Y. cancer source"
that we discussed in the last chance news were incorrect.
He writes:

The female breast cancer death rate for the US for the
period 1992-1996 was 25.4 deaths due to breast cancer
per year per 100,000 females of all ages (not "25.4%"
as stated.) The corresponding rates for Nassau County
and Suffolk County were 30.5 and 31.1, respectively.

As the District of Columbia example shows, care must be taken in
interpreting a significant result. How do you think they go about
trying to decide the cause of a significant increase in cancer
rates at a specific location?
<<<========<<

>>>>>==============>
The next article was provided by Norton Starr.

Year 2000 computer problems may get an alibi.
The New York Times, 14 December, 1999, C1
Barnaby J. Feder

In today's world, we rely on electronics in many ways. It is well-
known that complicated systems, such as telephone service and
electric power distribution, are subject to occasional problems.
This article reports on the breakdown rates for many activities.
Such information will help determine whether problems that occur
on January 1, 2000 are the result of Y2K problems.

Some examples of failure rates are: Ten percent of automated
teller transactions fail on their first attempt, usually because
of customer errors; in the last five years, tens of thousands of
residents of Canada and the United States have lost power in late
December or early January; emergency 911 service is disrupted
somewhere in the U. S. on the average of once per week; pipelines
carrying hazardous materials averaged 16 reportable disruptions
for the period of December 31 to January 3 over the last 3 years.

These rates form a 'baseline' to which the actual rates of
different types of problems occurring on January 1 will be
compared. While such information will do little to convince the
average person that a problem is not caused by Y2K, it will help
the authorities identify serious Y2K problems more quickly.

It is less clear whether the authorities will know how to use
these baseline rates. William Ulrich, a Y2K expert in Soquel,
California, says that "most people will have experts who know
what's normal in their command posts but 90 percent will be doing
their assessments based on gut feeling."

The article concludes with a somewhat mysterious quote from
another Y2K expert, Ann Coffou, in Cambridge, Massachusetts: "Too
much information is just as bad as not enough."

Discussion Questions:

(1) What is the difficulty of too much information in such a context?

(2) What is a useful way to combine the insight of experts relying on
their gut feeling, with modern statistical methods?

(3) Is it reasonable to think that there will be fewer problems over the
period considered this year than there were on previous years?
<<<========<<

>>>>>==============>
Is complexity interlinked with disaster? Ask on Jan. 1.
The New York Times, 11 December, 1999, B11
Laurence Zuckerman

This article describes a theory, called "normal accident theory," that
will be tested during the first few days and months of the year 2000. In
a 1984 book, "Normal Accidents: Living With High-Risk Technologies," the
author, Charles Perrow, argued that disasters such as the near meltdown
of the Three Mile Island nuclear reactor and the explosion of the Space
Shuttle Challenger should not be thought merely to be the result of
"human error." This theory posits that the emergence of more and more
intricate and interconnected systems causes such accidents, hence they
should be thought of as "normal accidents".

Of course, the reason for this article at this time is that it is
expected that many complicated systems will be tested by the date change
on January 1, 2000. By the time you read this, much will have been
reported about how well or how poorly such systems fared.

A competing view of systems is called the "high-reliability" view.
People who ascribe to this view believe that by building many backup
systems, safety can be enhanced. The normal accident theorists argue
that the existence of complicated backup systems can actually increase
the likelihood of an accident.

It is very interesting to consider the area of commercial aviation with
respect to the above theories. Although it would seem that flying is an
inherently risky system, in fact it is highly reliable. This area
therefore seems to support the high-reliability view. The National
Transportation Safety Board and the Federal Aviation Administration can
be thought of as backup systems, and of course are quite intricate in
their workings. The planes themselves are extremely complicated, and
are filled with backup systems. The reader might consider how a normal
accident theorist would respond to the apparent facts that aviation is
complex and yet safe. In this connection you might like to read the
article "Blowup" by Malcolm Gladwell, The New Yorker, Jan. 26, 1996, pp.
32-36. (see
Chance News 5.02).

DISCUSSION QUESTIONS:

(1) What do you think Charles Perrow learned from the Y2K experience?

(2) Do you agree that airplane experience seems to support the
"high-reliability" theory? What do you think Charles Perrow would say
about this?
<<<========<<

>>>>>==============>
Dan Rockmore suggested the following article from our local
newspaper:

Tough times.
Valley News, Dec 12, 1999, D1
Bruce Wood

This article is based on an interview with the Dartmouth football
coach John Lyons. In his first six years as coach the Dartmouth
football team did very well, winning two conference titles and
being a serious contender in the other years. However, in the
past two years the Dartmouth team has had a 3-11 record in the Ivy
League conference. In this interview, Lyons discusses factors that
he feels played a role in this dramatic change in the fortunes of
the football team. Lyons discusses a number of factors but the
most interesting from our point of view has to do with the effect
of the recent increase in the average SAT scores at Dartmouth.

In the early eighties the Ivy League established a set of rules
for recruiting athletes in the so called money sports: Football,
men's basketball, and hockey. Athletic scholarships have never
been allowed in the Ivy League but this agreement added an
academic requirement. We had difficulty finding an exact
formulation of these rules and so have relied on the description
given in the book: "A is for Admission", by Michele A. Hernandez,
Warner Books, 1997. Hernandez is a former Dartmouth admission
officer.

The new requirement involved the introduction of an academic index
defined as follows:

The academic index (AI) is the sum of three factors:

(1) the average of the student's highest SAT math and
verbal scores each rounded to two decimal places,

(2) the average of the student's three highest SAT
subject tests (also rounded to two decimal places) and

(3) the student's converted rank score (CRS).

The CRS is computed by a complicated algorithm and is based on the
student's high school rank in class or whatever related
information the school gives.

The AI is at most 240 and the Dartmouth average in 1997 was around
212. Under the Ivy League agreement the money teams must maintain
an average academic index which is at most one standard deviation
below the college's average academic index and individual recruits
must have an academic index of at least 169. Hernandez remarks
that football has more specific "bands" (or ranges of athletes
that it can accept) related to the individual colleges average
academic index.

The coaches submit their lists to the admission office and get
feedback in the form of likely, unlikely, possible etc. On the
basis of this they adjust their lists and submit new ones -- a
process that Hernandez calls "long and tiresome" both for the
coaches and the athletes.

The article points out that, until recently, the average SAT
scores of Dartmouth students was between those of Harvard,
Princeton and Yale, and those of Brown, Columbia, Cornell, and
Pennsylvania. This gave them a nice niche for the "bands" from
which they could recruit. But alas, along came a new president
James Freedman who was determined to raise the intellectual
climate of Dartmouth. He succeeded so well that now the average
SAT scores are comparable to Harvard, Princeton and Yale, and the
coach feels that this has makes it harder for them to recruit good
football players.

DISCUSSION QUESTIONS:

(1) Hernandez writes:

Have you ever wondered why you'll see a really brilliant
student sitting on the bench but not seeing much playing
time?

What do you think she has in mind?

(2) According to the US News & World Report college issue the
25th-75th percentiles for SAT scores are Dartmouth (1,350-
1,520),Princeton and Yale (1,360-1,540) and Harvard (1,400-1,580)
while below these we have Cornell (1,260-1450), Columbia (1,290-
1,490), Brown (1,290-1,500) and Penn (1,300-1,480) On the basis of
this do you think there is much difference in the standard
deviations for such scores between the schools.

(3) Obviously, the coach would like to see as large a standard
deviation as possible. Does this mean that, in fact, he should
help the admission office recruit really brilliant students
whether or not they are interested in athletics?
<<<========<<

This article reports on a new paradox discovered recently by Juan
Parrondo. We start with two games, each of which is biased against
the player. Game A consists of flipping a coin that has
probability 1/2 - c of coming up heads, where c is a small
positive number (in the article, c is taken to be .005). The
player wins $1 if the coin comes up heads; otherwise she loses $1.
In Game B, there are two coins; the first has probability 1/10 - c
of coming up heads, and the second coin has probability 3/4 - c of
coming up heads. If the player's current holdings are a multiple
of 3, then she next tosses the first coin; otherwise she next
tosses the second coin. In either case, she wins or loses $1
depending upon whether the coin comes up heads or not.

Game A is clearly biased against the player. It is not obvious,
but it is the case, that Game B is also biased against the player.
We will give an intuitive argument, and then a more mathematically
rigorous one. The key quantity to estimate is the long-term
probability of winning any particular flip. If, in the long run,
the player's holdings are 0, 1, or 2 (mod 3) with probabilities
p_1, p_2, p_3, then the probability of winning a particular flip
is just

(1/10 - c) p_1 + (3/4 -c) p_2 + (3/4 - c) p_3.

This is just the weighted average of the heads probabilities of the two
coins, where the weights are the percentages of times that the holdings
are 0, 1, or 2 (mod 3).

The intuitive argument runs as follows. If the holdings are 0 (mod 3),
then it is very likely that after the next flip, the holdings will be 2
(mod 3). If the holdings are 1 (mod 3), then it is very likely that
after the next flip, the holdings will be 2 (mod 3). Finally, if the
holdings are 2 (mod 3), then it is very likely that after the next flip,
the holdings will be 0 (mod 3). From this it is reasonable that the
holdings are much more likely to be either 0 or 2 (mod 3) than 1 (mod
3). But from this it follows that the probability of winning any
particular flip will perhaps be not much more than the average of (1/10
- c) and (3/4 - c). The average of these two numbers is 17/40 - c, which
is less than 1/2, so perhaps the probability of winning any particular
flip is also less than 1/2. In this case, the game is unfavorable.

Parrondo's paradox is that if the two games are played alternately, then
the composite game is favorable to the player. Different sequences of
plays of the two games lead to different biases in favor (or against)
the player. For example, the game is more favorable if the sequence
repeats the block A, A, A, B, B, than if it repeats the block A, B. In
addition, if the games are played according to a random sequence (with
probability p of playing Game A), then for many values of p (including p
=1/2), the composite game is favorable.

We now give a more rigorous argument for the statement that Game B is
unfavorable. This argument can be generalized to the case where the
games are played according to a random sequence. We consider Game B to
be a Markov chain, with states 0, 1, and 2, corresponding to the
holdings (mod 3). The various transition probabilities, i.e. the
probabilities of moving from one state to another, are given by the
probabilities that the two coins come up heads or tails. For example,
the probability that the chain moves from state 0 to state 2 is (9/10 +
c).

Standard Markov chain theory tells us that in the long run, the chain
will be in the various states certain fractions of the time; these
fractions are given by the fixed vector of the chain, which in this case
is approximately (.384, .154, .462). (Note that these fractions are in
line with our intuitive argument above.) Using these fractions, one can
compute that the probability of winning any particular toss, in the long
run, is about .4957. Thus, the game is unfavorable.

If the two games are played randomly, then we again have a Markov chain,
this time with 6 states; the states are labeled by the game that will
next be played (A or B) and the holdings (mod 3) (either 0, 1, or 2).
If, for example, the games are equally likely to be played, then one can
compute that the probability of winning any particular toss, in the long
run, is about .5079. This means that the game is favorable.

Here you find the following explanation for why they are
interested in these games:

Brownian ratchets can be used to harness the random
thermal fluctuations of molecules or very small
particles to get directed movement. If we introduce
a couple of games originally devised by Parrondo,
we can see that their mechanics work in a very
similar fashion to that of the Brownian ratchet.

To say that women make less than men mainly
because of time taken to rear children, as
you implied in your column ignores mountain
of research on why the wage gap persists.
Will you please address this issue again?

Consider this: if [women's] work is equal, why
aren't employers slashing their payroll costs by
hiring women instead of men? In a free market,
businesses are highly competitive and if they're
paying men more than women -- there must be a
reason. The most important question is "What is
that reason?

She invites here readers, men and women to fill out a survey about the
difference of the sexes on their jobs. Most of the questions have
three possible answers

(a) a women

(b) a man and

(c) it makes no difference at all.

Here are four such questions:

Whom would you rather hire as a full-time
baby-sitter while you work?

Whose voice do you trust more when you ask for
computer support?

Whom would you prefer to pilot your plane when you
travel?

Whom would you prefer to perform your heart surgery?

DISCUSSION QUESTION:

What do you think Marilyn expects to learn from such a survey? Will it
add significantly to the mountain of research on this issue?
<<<========<<

Historian Margo Anderson and statistician Stephen Fienberg combine
forces to give us a historical perspective of the Census from the first
census in 1790 to the upcoming Census 2000.

The book begins with a history of the Census. The story starts with the
founding fathers' establishing a representative form of government that
required a census to make it work. We learn that the political problems
we face today with the census have been with us from the very beginning:
Who should be counted? How should they be counted?

We also learn that throughout the history of the census leading
statisticians have played a major role in trying to answer these
questions. It would be hard to think of another statistical
problem that has received as much attention by the statistical
community as that of carrying out the census. This is remarkable
considering that they work under the constraint of continually
changing political winds.

Following the historical introduction, the authors give a detailed
account of the last three censuses. Of course the undercount
problem and the associated legal battles play a major role in this
account.

The reason that the undercount problem is a political issue is
fairly obvious since adjustment tends to increase the count of
minorities and minorities tend to vote Democratic. Also large sums
of Federal money to the states are affected by the outcome of the
census.

The statistical issues are more complicated but center around
whether the assumptions required for the methods used are
satisfied and whether the attempts to adjust the census cause more
errors than they correct.

In the 1990 court cases we find-well known statisticians on each
side of the issue. For example, in a New York lawsuit that led to
not using the adjustment in 1990 we find Steven Fienberg, Ralph
Rolf, and John Tukey supporting the use of the undercount
adjustment and Paul Meier and David Freedman testifying against
its use.

For the Census 2000 the Supreme Court has ruled that sampling
methods cannot be used for apportionment. However, evidently it
can be used for redistricting and determining the amount of money
each state receives in Federal grants.

Thus the Census Bureau plans to do a traditional enumeration and
present this information for apportionment to the President on
December 31, 2000 as required by law. They will then carry out the
undercount adjustment and give what they believe will be a more
accurate count to the states for redistricting by March 31, 2001.

For any of us wishing to follow these developments it is advisable
to have a clear idea of how the adjustment for the undercount is
carried out. The authors provide this information in their book.
However, as a test of whether we understand how it works, we will
give our understanding of how the Census Bureau plans to carry out
the adjustment.

The Census Bureau divides the population into blocks with a block
having about the number of housing units you would find in a
typical city block. For the purpose of estimating the undercount,
the country is also divided into groups that are similar with
respect to race, Hispanic origin, region of the country, gender
and whether the family owns or rents the house. There are about
1300 such sub-regions called post strata. The role they play will
be clear soon.

After the traditional enumeration is completed for the census, an
independent second enumeration called the post enumeration survey
will be carried out independently of the census for a stratified
sample of about 60,000 blocks. Within a particular post strata the
two enumerations are compared and the people who appeared in both
are identified. Suppose, for example, that the census count for
this strata was 10,000 and for the post enumeration survey it was
9,000. Assume that there were 7,000 who were counted in both the
census and the survey. Then since 7/9 of those counted in the
census were counted in the survey it is assumed that the census
identified 7/9 of the people in this strata. Thus the adjusted
estimate for the number in the strata would be 10,000*9/7 =
12,857.

This estimate is based on the classical capture-recapture method
usually described in terms of estimating the number of fish in a
lake. A sample of fish is caught and tagged and then later a
second sample is caught and the proportion of fish in the lake is
assumed to be the same as the proportion of the fish that are
tagged in the second sample.

This model assumes that that within each sample all the fish have
the same probability of being captured. Also the events of being
captured in the two samples are independent. It is further assumed
that all the counts are accurate and the tags do not fall off so
the identification is correct etc.

For the census application of capture re-capture the census plays
the role of the first sample (the tagged fish) and the post
enumeration survey the role of the second sample (the re-captured
fish). The assumption that within each sample each person has the
same probability of being counted is called the assumption of
heterogeneity. The independence assumption is related to a concept
called correlation bias. It is hoped that, at least within a post
strata, these assumptions are reasonably justified, though the pro
and anti adjustment statisticians do not agree on this point.

Some of the other assumptions are clearly not satisfied so the
Census Bureau has to adjust for this. The Census Bureau cannot use
unique identification markers such as social security numbers
since they feel that this would make some people reluctant to be
counted. Thus they must compare facts about the people identified
-- where they lived, their race, gender, etc. They can easily make
a mistake. People can be counted twice in the census -- a college
student might be counted both at his home and at his school. Also,
between the time of the census and the post enumeration survey
some people die, some babies are born and some move out of the
area of the sample. The Census Bureau has to deal with these
problems.

In an attempt to deal with many of these problems the Census
Bureau re-examines all census enumerations in the sampled blocks
to obtain a true count for this area for the census. Let's return
to our example in which the census count was 10,000 and the post
enumeration survey counted 9,000 people. After checking for
duplications and other errors 9800 remain for the true census
count. Then our estimate of 10,000 should have first been reduced
by a factor of 98/100. This gives us an overall adjustment factor
of (9/7)*(98/100) = 882/700 = 1.26 Thus our adjusted number for
this post strata would be 10,000*1.26 = 12,600.

This procedure is carried out to obtain an adjustment factor for
each post strata. Sampling causes excessive variation in these
factors so a final statistical process of "smoothing" is carried
out. These factors are then applied to adjust the estimate for the
number of people in each block in the country. From this we can
obtain adjusted census counts for cities, towns, or any particular
area needed by simply adding the block counts.

It is generally admitted that these estimates may not be too
accurate at the block level but when the results are added for
larger groups many of these inaccuracies will cancel out. Critic
Kenneth Darga provided a novel way to check this claim as it
related to the 1990 census. He looked at the proportion of boys
and girls under the age of 10 in various groups. He looked at nine
demographic groups. He found that before adjustment the proportion
of boys were all 51 percent as to be expected. However, for the
adjusted counts these proportions varied from 48 percent to 56
percent. This led him to conclude that the adjusted counts were
not accurate.

This book will be read by science writers, lawyers, judges and
politicians as the inevitable court cases for the census 2000
approach. We hope that people who read this book will come away
with the feeling that there has to be a better way than fighting
it out in the courts to figure out how to carry out this important
and challenging statistical task.
<<<========<<

A study in the current issue of JAMA (Journal of the American Medical
Association) focused on three major risk factors for heart disease:
smoking, high cholesterol and high blood pressure. The conclusion was
that people at low risk for each of these factors had from 6 to 10 years
increased life expectancy. Low risk was defined as non-smoking,
cholesterol less than 200 milligrams per deciliter and blood pressure
below 120-over-80. The researchers were encouraged because these appear
to be realistic goals for much of the population.

Data were obtained from 360,330 men and 6229 women who had enrolled in
two major prospective studies beginning between 1967 and 1972. The
result was the first dataset large enough to include a substantial
number people at low risk for all three categories, and with follow-up
long enough to include sufficient numbers of deaths to estimate life
expectancies. Men who were between 18 and 39 years old when they
enrolled and met the low risk criteria were estimated to have between
6.3 and 9.5 years of additional life expectancy compared to other men
their age. Men aged 40 to 59 had six additional years, and women aged
40 to 59 had 5.8 additional years. The benefits extended across
socioeconomic and racial groups.

DISCUSSION QUESTION:

According to the article: "The studies did not record dietary or
exercise habits, but Stamler and his colleagues suggested that
cholesterol level and blood pressure may be not only risk-reducing
factors in themselves, but also indicators of healthier lifestyles and
more exercise." Does this mean that reducing cholesterol is beneficial
in and of itself, without changes lifestyle or exercise? How would they
know this?
<<<========<<

As 1999 draws to a close, many people are looking at temperature records
and speculating about global warming. Another potential indicator of
warming is the disappearance of Arctic ice. An international study
appearing in the journal Science has combined 46 years of data from 5
independent data sets, which include both ground-based measurements and
satellite observations. The data show that Arctic sea ice is decreasing
by 14,000 square miles per year on average.

The authors of the study infer that the melting is attributable to human
activity rather than natural variability in the Arctic climate. They
report only a 2% chance that the melting over twenty years represents
normal climate variation, and a 0.1% chance that the whole 46 year
record is attributable to normal variation. Furthermore, the observed
data were found to be consistent with figures generated by computer
climate models that simulate the effects of greenhouse gas emissions.

The article points out that 50 years is a short time period for
assessing global climate changes, so it is hard to assess whether the
observed melting is unusual. The lack of longer range data places
additional importance on the comparison with computer models. The
particular model used here was developed by the National Oceanic and
Atmospheric Administration's Geophysical Fluid Dynamics Laboratory in
Princeton, and is widely respected. Still, the article reports that
this the first time the model has been specifically applied to Arctic
ice. One critic of the study, Richard Moritz of the University of
Washington, said "I am not convinced that the natural variability of the
ice extent simulated by the model is realistic."

DISCUSSION QUESTION:

The melting rate is expressed in terms of area. Can you see any
potential difficulties with this?
<<<========<<

Does the full moon affect human behavior? This article quotes a number
law enforcement officials expressing belief in the so-called "werewolf
effect." An Illinois police lieutenant name d Michael Roberts says
"People are a little bit weirder. Nurses, cops, fireman--we all believe
it, whether it's been scientifically proven or not."

Medical workers in obstetrics also express belief that more babies are
born during full moons. The article quotes Betty Fennema of Provena St.
Joseph Medical Center. Fennema, who has spent most of her 34 year
career in obstetrics, says "I don't have any statistical data, just
practical knowledge. Those of us who have been around obstetrics a long
time just expect a rise in the census."

DISCUSSION QUESTIONS:

(1) What do you think of the distinction being drawn between "scientific
data" and "practical knowledge?" Propose some ways to collect
"scientific data" to check these theories.

(2) According to the article, "Illinois State Police Sgt. Jeff Hanford
says it's superstition, but police expect to be busy [during a full
moon]. It's usually after the fact that we notice it. We'll have a
busy night and then someone will notice it's a full moon." Comment.
<<<========<<

Observational data indicate that diets high in fruits and vegetables
containing beta carotene are associated with lower incidence of cancer
and heart disease. But is beta carotene itself responsible? In
Chance
News 5.02, we described research that questioned the value of beta
carotene dietary supplements.

There is similar news in today's issue of the Journal of the National
Cancer Institute. It reports results from a four-year study involving
19,939 women who received beta carotene and 19,937 who took a placebo.
During the study period, there were 378 cancers in the beta carotene
group and 369 in the control group. Over the same period, there were 42
heart attacks in the beta carotene group and 50 in the placebo group.
These differences were not found to be statistically significant.

DISCUSSION QUESTION:

Saying there were "378 cancers" in the treatment group does not
make it clear whether 378 different people group got cancer, or if some
people suffered multiple cancers. Does it matter?
<<<========<<

>>>>>==============>
We woz wrong.
The Economist, 18 December 1999, 47-48

Newspapers often print predictions, but follow them up much less often.
Here the Economist confesses two conspicuous blunders: first a string of
warnings the American stock market bubble was ready to burst, and second
a prediction that oil prices were headed for further declines. In its
cover story of 6 March 1999, the magazine reported that the world was
"drowning in oil." At that time, the price of crude oil had dropped to
$10 a barrel, and the story predicted that it might soon drop as low as
$5. Alas, a mere four days later, OPEC agreed to cut production;
within two weeks the price had risen 30%. By December, it had reached
$25 a barrel!

How, the article asks, could the prediction be so far off? Three partial
explanations are offered. First, the oil prediction was not made in
isolation; it was based in part on the idea that slow growth in the
world economy would keep demand for oil down. Thus, failing to
anticipate the Asian economic recovery contributed to the error on oil
prices. Second, the forecast included speculation that the Saudis might
not go along with OPEC, but would instead increase their own production
to enhance revenues. But there are pitfalls in trying to guess what the
Saudi leaders are thinking, given that their decision-making process
does not include public debate. Third, the forecast itself may have
increased the resolve of the OPEC leaders!

Given the difficulties in making predictions, the article raises the
rhetorical question of whether it might be better to simply give up.
But it points out that predictions are an essential part of policy
discussions. Two complicated examples are discussed at some length:
NATO's decision not to commit ground troops to drive the Serbs out of
Kosovo, and President Clinton's decision not to resign during the
scandal that culminated in his impeachment trial. The article notes
that every opinion expressed about a policy issue implicitly contains a
forecast about the consequences of either following or not following
some course of action. It concludes that publications like the
Economist should set for themselves the goal of making their predictions
explicit in order to hold them up for debate.

DISCUSSION QUESTION:

Take a current newspaper story about policy and identify the
predictions implicitly or explicitly made there (for example, you might
consider the Washington Post article on Arctic sea ice presented in this
Chance News). How well are they supported?
<<<========<<